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Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer.
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ayaz  
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 More options May 12, 4:40 am
Newsgroups: alt.fan.countries.north-korea, soc.culture.japan, soc.culture.burma, soc.culture.somalia, soc.culture.zimbabwe
From: ayaz <hackedyetag...@japan.com>
Date: Mon, 12 May 2008 01:40:57 -0700 (PDT)
Local: Mon, May 12 2008 4:40 am
Subject: Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer.

Brain-scale simulation of the neocortex on the IBM Blue Gene/L
supercomputer.

Djurfeldt, M.1 mik...@djurfeldt.com
Lundqvist, M.1
Johansson, C.1
Rehn, M.1
Ekeberg, Ö.1
Lansner, A.2

1Computational Biology and Neurocomputing Group, Royal Institute of
Technology, S-100 44, Stockholm, Sweden
2Computational Biology and Neurocomputing Group (CBN), Royal Institute
of Technology, S-100 44, Stockholm, Sweden

IBM Journal of Research & Development; Jan2008, Vol. 52 Issue 1/2,
p31-41, 11p

Biologically detailed large-scale models of the brain can now be
simulated thanks to increasingly powerful massively parallel
supercomputers. We present an overview, for the general technical
reader, of a neuronal network model of layers II/III of the neocortex
built with biophysical model neurons. These simulations, carried out
on an IBM Blue Gene/Le supercomputer, comprise up to 22 million
neurons and 11 billion synapses, which makes them the largest
simulations of this type ever performed. Such model sizes correspond
to the cortex of a small mammal. The SPLIT library, used for these
simulations, runs on single-processor as well as massively parallel
machines. Performance measurements show good scaling behavior on the
Blue Gene/L supercomputer up to 8,192 processors. Several key
phenomena seen in the living brain appear as emergent phenomena in the
simulations. We discuss the role of this kind of model in neuroscience
and note that full-scale models may be necessary to preserve natural
dynamics. We also discuss the need for software tools for the
specification of models as well as for analysis and visualization of
output data. Combining models that range from abstract connectionist
type to biophysically detailed will help us unravel the basic
principles underlying neocortical function.


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